The present invention relates generally to the field of non-invasive imaging and more specifically to the field of computed tomography (CT) imaging and inspection systems. In particular, the present invention relates to boundary-based CT reconstruction techniques for use in CT metrology.
Various types of measurement systems such as computed tomography (CT), coordinate measuring machines (CMM), laser-based profilometry, light gauge, infrared and others are used in industrial inspection processes for a wide variety of applications, such as for measuring wall thickness or for identifying defects in manufactured parts. Each measurement/inspection system has advantages and disadvantages. Modalities such as CMM and laser-based profilometry typically measure external surfaces with high accuracy, but cannot measure internal features unless the object is cut open. To date, CT is the most versatile of the measurement/inspection systems for revealing both the internal and external structures of industrial parts in a non-destructive manner. Because of their ability to provide internal as well as external measurements, CT based techniques may facilitate processes such as reverse engineering, rapid prototyping, casting simulation & validation, tire development, first article inspection, ceramic porosity inspection, process validation, parts qualification and defect detection, as well as others. However, CT based techniques may also have a relatively low inspection accuracy, which may deter their widespread use.
For example, in the area of reverse engineering, CT may be unsatisfactory for capturing detailed external surface features, which may be crucial for capturing the design intent. Factors affecting CT accuracy in this regard include beam-hardening, partial volume effect, scattering and off-focal radiation, as well as others. Thus, in order to improve CT inspection accuracy, more effective methods are needed for removing artifacts such as these.
These types of artifacts may arise for a variety of reasons, including the type of CT reconstruction process employed. For example, filtered backprojection (FBP) is a common technique for reconstructing CT images because of its fast computation and ease of implementation. However, because FBP is based on an oversimplification of the CT data acquisition into an ideal mathematical transform such as parallel beam Radon transform, Fan Beam transform, cone beam transform or other geometric line integral transform depending on the particular acquisition geometry, the reconstructed image may suffer from artifacts such as beam hardening and partial volume as discussed above, contributing to degraded image quality and inspection accuracy. Furthermore, pre-correction of the data prior to performing an FBP reconstruction may provide full correction of the associated artifacts.
Iterative CT image reconstruction addresses some of these image quality issues. These techniques may be based on different mathematical principles, such as the statistical approach of maximum likelihood or the least squares approach, for example. Iterative methods allow incorporation of a dedicated forward model of the data acquisition and physics of the CT scan in the reconstruction algorithm and iteratively determine the image, thereby improving the accuracy. However, iterative reconstruction approaches may be computationally inefficient, typically having long computation times due to their intensive computational requirements. Furthermore, both FBP techniques and iterative reconstruction techniques may have limited accuracy due to their reliance on pixel grid representations and/or image segmentation.
It is therefore desirable to provide an improved inspection method that accurately captures both the internal and external features of an object to be inspected in an inexpensive manner that is computationally efficient and time efficient.